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How do SAM Controllers Self Learn?

How do SAM Controllers Self Learn?

SAM Controllers are designed with 3DFS’ flexible and efficient computing methodology, Task Oriented Optimal Computing. It is a software-based computing methodology that can be embedded into any processor to extract orders of magnitude more processing power from them with a fraction of the power consumption traditionally used.


This significant increase in efficient computing capacity opens up new approaches to intelligent sensing where all data error correction methods can be accomplished upon data acquisition, producing secure streams of error free, digitized data. All sensors and data inputs are synchronized by timing providing an exact Real-Time snapshot of the entire system at that moment in time, the ideal bedrock of operating data, both Real-Time and historical.


Data is fed into an advanced mathematical model where an intense analysis of the operating data is performed. There are numerous data mining algorithms functioning in parallel with a particular focus on any nonlinear processes that are detected. The data extraction gleaned from the deep analysis of all nonlinear events is one of the key focuses of the processing because it reveals a more thorough level of system operation and ensures proper decision making.


The speed at which this data processing occurs is what provides a substantial advantage over the traditional methods of logical control. All SAM control decisions are based on analytics derived from deep Real-Time modeling of error free data making the best possible decision at the correct moment every time during operation.


It does not end with the control decision, the system must be aware of the effects of the decision in order to learn. The system must have feedback on all controlled actions to ensure completion and to analyze the decision results, comparing it against the known model. All feedback from any control decision goes back into the mathematical model providing the system with near instant awareness of the effects of its control actions.


The deep Real-Time analysis of these effects is the cornerstone information that allows the controller to self-learn, improving its performance and efficiency over time and continuing to deliver value long past the purchase point.


Pumps, machines and systems are designed to operate in a certain way and frequently the control systems and humans operating do not have the information or tools to maximize the performance. With proactive, self-learning control systems, pumps and machinery can autonomously operate while continuously improving its performance and self-report problems thereby reducing human interaction and allowing business owners to focus on their bottom line instead of the expense line.


The SAM Controls methodology is a new generation of advanced energy efficiency technology that is applicable to any system requiring sensing and control and demanding precision and security. Learn more at SAMControllers.com.


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